Agenda

  • Introduction to time series analysis and forecasting
  • Time series objects - introduction to the time series classes and their attributes
  • Descriptive analysis of time series
  • Linear regression-based forecasting models
  • The ARIMA family of models

Assumptions

  • Some background in R
  • Basic knowledge in probability
  • Familiar with linear regression

Why R?

  • Statistical programming language
  • A vast amount of packages for time series analysis
  • The forecast package (and soon the fable package)

Admin

Workshop material

All today’s slides, code, and rmarkdown files are available on GitHub

Downloading the workshop material from the terminal:

git clone https://github.com/RamiKrispin/Time-Series-Workshop.git

Or lunch it from a docker container:

Introduction to time series analysis and forecasting

Time series data

Time series data - is a sequence of values, each associate to a unique point in time

Regular time series - is a sequence of observations which were captured at equally spaced time intervals (e.g., every month, week, day, hour, etc.)

Irregular time series - or unevenly spaced time series, is a sequence of observations which were not captured on equally spaced time intervals (for example rainy days, earthquakes, clinical trials, etc.)

Note: typically, the term time series data referred to regular time-series data.

Examples of time series data

Time Series Analysis

Time series analysis - is the process of extracting meaningful insights from time series data in order to learn about the past and predict the future

With time series analysis, you can answer questions such as:

  • How many vehicles, approximately, going to be sold in the US in the next 12 months?
  • What will be the estimated demand for natural gas in the US in the next five years?
  • Generally, what will be the demand for electricity in the UK during the next 24 hours?